Insider fraud occurs when an enterprise insider, e.g., an employee of a given enterprise or company, abuses his or her access to enterprise resources to take actions that harm the enterprise, enrich the enterprise insider, or both. Enterprise insiders often are “trusted” users who need access to sensitive information and sensitive company resources in order to perform their jobs. Insider fraud can be either intentional or unintentional; some insiders are maliciously trying to commit fraud, while others simply do not understand security rules or make mistakes. Examples of insider fraud include stealing trade secrets, embezzling money, stealing customer identities, disclosing customer information, and engaging in risky trading in the name of the enterprise.
Enterprises face significant risk from the intentional and unintentional actions of insiders. Incidents caused by insiders can have a devastating impact on an enterprise. However, most security solutions focus primarily on external threats; not on threats posed by enterprise insiders. While some technologies are designed to detect and combat internal fraud, these technologies generally provide a patchwork of features without fundamentally managing risk. For example, data loss prevention (DLP) tools attempt to stop external leakage of specific sensitive data. These DLP tools analyze outgoing data to identify specific patterns corresponding to, for example, social security numbers or credit card numbers. However, these DLP tools have a limited context for detecting and blocking complex data types and can often be defeated by simple evasive tricks. As another example, content filtering solutions block specific types of suspicious activities such as file transfers, use of personal webmail accounts, and downloading of unauthorized software. However, these filtering solutions are not comprehensive. Identity and access management (IAM) tools provide tools to allow granular control of user access to systems, but cannot easily identify malicious activity by authorized users. Password management and auditing tools can detect compromised passwords, but have few abilities beyond that limited functionality. Database activity and monitoring tools monitor user access to databases but are difficult to tune and require specialized expertise to determine what is malicious. Physical security systems can detect access violation attempts, but have limited analytical functions. Other security technologies such as encryption, USB device blocking, and security event monitoring provide protection from specific threats, but do not provide more general protection.
As another example, security information and event management (SIEM) systems can detect certain types of suspicious behavior, but have to be carefully configured to avoid false positives. Network access control systems can detect and block enterprise insiders that want to launch malware within an enterprise before the insider accesses the network, but once the user is authenticated and on the network, they have little effect. Fraud detection systems can detect some fraud but require complex integration and tuning, and often do not integrate well with traditional security systems.
The disclosed embodiments are configured to overcome these and other problems.